We use the classification loss \(L_{\mathrm {cls}}\) , regression loss \(L_{\mathrm {reg}}\) , and direction classification loss \(L_{\mathrm {dir}}\)  from PointPillars [1]} for 3D object detection. The total loss of our network is the combination of the depth and 3D detection losses:
\(L = \lambda _{\mathrm {depth}}L_{\mathrm {depth}} + \lambda _{\mathrm {cls}}L_{\mathrm {cls}} + \lambda _{\mathrm {reg}}L_{\mathrm {reg}} +\lambda _{\mathrm {dir}}L_{\mathrm {dir}}\) 
